{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f491a007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Autocorrelations of series ‘yarn_yield’, by lag\n",
      "\n",
      "     0      1      2      3      4      5      6      7      8      9     10 \n",
      " 1.000  0.914  0.843  0.767  0.695  0.609  0.544  0.476  0.395  0.321  0.242 \n",
      "    11     12     13     14     15 \n",
      " 0.147  0.058 -0.033 -0.108 -0.168 \n"
     ]
    },
    {
     "data": {
      "image/png": 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DFi3rx5gwYNavzZAACagpy5eGLMmDHl5eUzZsyYM2dOUVFRp06d2rVrl0qlNm/e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\nQmCC",
      "text/plain": [
       "Plot with title “Series  yarn_yield”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Partial autocorrelations of series ‘yarn_yield’, by lag\n",
      "\n",
      "     1      2      3      4      5      6      7      8      9     10     11 \n",
      " 0.914  0.050 -0.065 -0.021 -0.129  0.065 -0.034 -0.139 -0.019 -0.094 -0.166 \n",
      "    12     13     14     15 \n",
      "-0.038 -0.116  0.010  0.053 \n"
     ]
    },
    {
     "data": {
      "image/png": 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1b8UZ988sk111yza9euA+yzYcOG4AMtAICMV37YjR49esCAAXuH3ZNPPjl37tzH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\nAEVQ7AAAABRBsQMAAFDEPwCPNCeGoao+qwAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Series  yarn_yield”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 实验1\n",
    "library('readxl')\n",
    "# 读取excel\n",
    "my_excel <- read_excel('data1.xlsx')\n",
    "# 读取excel产量这一列，且列索引从1985开始\n",
    "yarn_yield <- ts(my_excel$产量,start=1985)\n",
    "# 绘出该序列的时序图\n",
    "#plot(yarn_yield)\n",
    "\n",
    "# # 导入tseries\n",
    "# library(tseries)\n",
    "# # 检查序列纯随机性，根据p值大小判断是否为白噪声序列\n",
    "# print('滞后6阶');Box.test(yarn_yield,6)\n",
    "# print('滞后12阶段');Box.test(yarn_yield,12)\n",
    "# print('滞后18阶段');Box.test(yarn_yield,18)\n",
    "# print('滞后24阶段');Box.test(yarn_yield,24)\n",
    "# 对序列进行一阶，二阶差分\n",
    "# yarn_yield_d1 <- diff(yarn_yield)\n",
    "# yarn_yield_d2 <- diff(yarn_yield,2)\n",
    "# # 对序列进行单位根检验，判断平稳性\n",
    "# adf.test(yarn_yield)\n",
    "# adf.test(yarn_yield_d1)\n",
    "# adf.test(yarn_yield_d2)\n",
    "# 画出原序列的自相关图，与偏自相关图\n",
    "print(acf(yarn_yield))\n",
    "print(pacf(yarn_yield))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "11634f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = data2, order = c(2, 0, 2))\n",
       "\n",
       "Coefficients:\n",
       "         ar1      ar2      ma1     ma2  intercept\n",
       "      0.8857  -0.3920  -1.3038  0.6082    84.1245\n",
       "s.e.  0.2156   0.1328   0.2028  0.1563     0.1118\n",
       "\n",
       "sigma^2 estimated as 6.931:  log likelihood = -479.97,  aic = 971.94"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Pierce test\n",
       "\n",
       "data:  fit2$residuals\n",
       "X-squared = 4.8745, df = 6, p-value = 0.56\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Pierce test\n",
       "\n",
       "data:  fit2$residuals\n",
       "X-squared = 6.7966, df = 12, p-value = 0.8708\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Pierce test\n",
       "\n",
       "data:  fit2$residuals\n",
       "X-squared = 9.2686, df = 18, p-value = 0.9532\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z8Zrw4zZ868f//+//3f/xmvDgkJCTY2NnZ2djdu3Pjqq6+k33s1PFnTq4NxPaZX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\ndgAAAArx/96cGxrR0MoZAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “p values for Ljung-Box statistic”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 实验二 ——————— ARMA模型建模与预测实验指导\n",
    "# （1）根据时序图判断序列的平稳性；\n",
    "# （2）观察相关图，初步确定移动平均阶数q和自回归阶数p；\n",
    "# （3）对某企业201个连续生产数据建立合适的模型，并能够利用此模型进\n",
    "### 要求\n",
    "# （1）深刻理解平稳性的要求以及ARMA模型的建模思想；\n",
    "# （2）如何通过观察自相关，偏自相关系数及其图形，利用最小二乘法，以及信息准则建立合适的ARMA模型；如何利用ARMA模型进行预测；\n",
    "# （3）熟练掌握相关R操作，读懂模型参数估计结果。\n",
    "# 2.1 绘制时序图\n",
    "\n",
    "# 导入readxl\n",
    "library('readxl')\n",
    "\n",
    "# 读excel\n",
    "my_excel2 <- read_excel('data2.xlsx',col_names=c('data'))\n",
    "\n",
    "# 读data列\n",
    "data2 <- ts(my_excel2$data,start = 0)\n",
    "\n",
    "# 根据时序图判断平稳性\n",
    "#plot(data2)\n",
    "#print(\"从图1我们可以看出，数据是平稳的\")\n",
    "\n",
    "# 2.2 绘制自相关图与偏自相关图，决定模型的p与q值\n",
    "# acf(data2)\n",
    "# pacf(data2)\n",
    "# print('我们从自相关与偏自相关图中看出，ACF拖尾，PACF拖尾')\n",
    "\n",
    "# 2.3 根据adf与padf的结果，建立模型\n",
    "#print('我们根据自相关图和p偏自相关图估计模型为ARMA（7，3）')\n",
    "fit2 <- arima(data2,order = c(2,0,2))\n",
    "fit2\n",
    "# 对残差进行Box检验，判断模型是否完全提取了信息\n",
    "Box.test(fit2$residuals,lag=6)\n",
    "Box.test(fit2$residuals,lag=12)\n",
    "Box.test(fit2$residuals,lag=18)\n",
    "# 因为残差的p值均大于0.05，所以残差为白噪声序列，ARMA模型已经完全提取完成\n",
    "# 再画出残差的adf与padf图\n",
    "tsdiag(fit2)\n",
    "# 残差也都自回归与零，说明模型建立良好\n",
    "\n",
    "# 对我们的模型进行预测\n",
    "# library(forecast)\n",
    "# fore<-forecast(fit2,h=10)\n",
    "# fore\n",
    "# plot(fore)\n",
    "# lines(fore$fitted,col=2,lty=2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c8f03fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1．实验内容：\n",
    "# （1）根据时序图的形状，采用相应的方法把非平稳序列平稳化； \n",
    "# （2）对经过平稳化后的1965年到2020年某地区进出口贸易总额数据建立合适的模型，并能够利用此模型进行进出口贸易总额的预测。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ecead4ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "A Time Series:<br><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>41.5</li><li>59.5</li><li>64.6</li><li>80.9</li><li>84.7</li><li>109.8</li><li>108.7</li><li>104.5</li><li>128.7</li><li>149.3</li><li>128.4</li><li>90.7</li><li>80.9</li><li>85.7</li><li>97.5</li><li>118.4</li><li>127.1</li><li>112.2</li><li>108.5</li><li>107.7</li><li>112.9</li><li>120.9</li><li>146.9</li><li>220.5</li><li>292.2</li><li>290.4</li><li>264.1</li><li>272.5</li><li>355</li><li>454.6</li><li>570</li><li>735.3</li><li>771.3</li><li>860.1</li><li>1201</li><li>2066.7</li><li>2580.4</li><li>3084.2</li><li>3821.8</li><li>4155.9</li><li>5560.1</li><li>7225.8</li><li>9119.6</li><li>11271</li><li>20381.9</li><li>23499.9</li><li>24133.8</li><li>26967.2</li><li>26854.1</li><li>29896.3</li><li>39273.2</li><li>42183.6</li><li>51378.2</li><li>70483.5</li><li>95539.1</li><li>116921.8</li></ol>\n"
      ],
      "text/latex": [
       "A Time Series:\\\\\\begin{enumerate*}\n",
       "\\item 41.5\n",
       "\\item 59.5\n",
       "\\item 64.6\n",
       "\\item 80.9\n",
       "\\item 84.7\n",
       "\\item 109.8\n",
       "\\item 108.7\n",
       "\\item 104.5\n",
       "\\item 128.7\n",
       "\\item 149.3\n",
       "\\item 128.4\n",
       "\\item 90.7\n",
       "\\item 80.9\n",
       "\\item 85.7\n",
       "\\item 97.5\n",
       "\\item 118.4\n",
       "\\item 127.1\n",
       "\\item 112.2\n",
       "\\item 108.5\n",
       "\\item 107.7\n",
       "\\item 112.9\n",
       "\\item 120.9\n",
       "\\item 146.9\n",
       "\\item 220.5\n",
       "\\item 292.2\n",
       "\\item 290.4\n",
       "\\item 264.1\n",
       "\\item 272.5\n",
       "\\item 355\n",
       "\\item 454.6\n",
       "\\item 570\n",
       "\\item 735.3\n",
       "\\item 771.3\n",
       "\\item 860.1\n",
       "\\item 1201\n",
       "\\item 2066.7\n",
       "\\item 2580.4\n",
       "\\item 3084.2\n",
       "\\item 3821.8\n",
       "\\item 4155.9\n",
       "\\item 5560.1\n",
       "\\item 7225.8\n",
       "\\item 9119.6\n",
       "\\item 11271\n",
       "\\item 20381.9\n",
       "\\item 23499.9\n",
       "\\item 24133.8\n",
       "\\item 26967.2\n",
       "\\item 26854.1\n",
       "\\item 29896.3\n",
       "\\item 39273.2\n",
       "\\item 42183.6\n",
       "\\item 51378.2\n",
       "\\item 70483.5\n",
       "\\item 95539.1\n",
       "\\item 116921.8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "A Time Series:  \n",
       "1. 41.5\n",
       "2. 59.5\n",
       "3. 64.6\n",
       "4. 80.9\n",
       "5. 84.7\n",
       "6. 109.8\n",
       "7. 108.7\n",
       "8. 104.5\n",
       "9. 128.7\n",
       "10. 149.3\n",
       "11. 128.4\n",
       "12. 90.7\n",
       "13. 80.9\n",
       "14. 85.7\n",
       "15. 97.5\n",
       "16. 118.4\n",
       "17. 127.1\n",
       "18. 112.2\n",
       "19. 108.5\n",
       "20. 107.7\n",
       "21. 112.9\n",
       "22. 120.9\n",
       "23. 146.9\n",
       "24. 220.5\n",
       "25. 292.2\n",
       "26. 290.4\n",
       "27. 264.1\n",
       "28. 272.5\n",
       "29. 355\n",
       "30. 454.6\n",
       "31. 570\n",
       "32. 735.3\n",
       "33. 771.3\n",
       "34. 860.1\n",
       "35. 1201\n",
       "36. 2066.7\n",
       "37. 2580.4\n",
       "38. 3084.2\n",
       "39. 3821.8\n",
       "40. 4155.9\n",
       "41. 5560.1\n",
       "42. 7225.8\n",
       "43. 9119.6\n",
       "44. 11271\n",
       "45. 20381.9\n",
       "46. 23499.9\n",
       "47. 24133.8\n",
       "48. 26967.2\n",
       "49. 26854.1\n",
       "50. 29896.3\n",
       "51. 39273.2\n",
       "52. 42183.6\n",
       "53. 51378.2\n",
       "54. 70483.5\n",
       "55. 95539.1\n",
       "56. 116921.8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "A Time Series:\n",
       " [1]     41.5     59.5     64.6     80.9     84.7    109.8    108.7    104.5\n",
       " [9]    128.7    149.3    128.4     90.7     80.9     85.7     97.5    118.4\n",
       "[17]    127.1    112.2    108.5    107.7    112.9    120.9    146.9    220.5\n",
       "[25]    292.2    290.4    264.1    272.5    355.0    454.6    570.0    735.3\n",
       "[33]    771.3    860.1   1201.0   2066.7   2580.4   3084.2   3821.8   4155.9\n",
       "[41]   5560.1   7225.8   9119.6  11271.0  20381.9  23499.9  24133.8  26967.2\n",
       "[49]  26854.1  29896.3  39273.2  42183.6  51378.2  70483.5  95539.1 116921.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library('readxl')\n",
    "# 读excel\n",
    "my_excel3 <- read_excel('data3.xlsx')\n",
    "\n",
    "# 读ckmy列\n",
    "ckmy <- ts(my_excel3$ckmy,start = 1965)\n",
    "\n",
    "# 绘图\n",
    "plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be3f526",
   "metadata": {},
   "source": [
    "# 时间序列\n",
    "总分100,18周周三，1点半考试\n",
    "\n",
    "## 题型\n",
    "1. 选择题10个（20分）\n",
    "2. 判断题10个（20分）\n",
    "3. 简答题4个（20）\n",
    "4. 计算题2个（每题多个小问）（25）\n",
    "5. 看图分析（15） # 根据回归进行相应解释\n",
    "## 重点\n",
    "1. 第一章没有重要知识\n",
    "2. 第二章\n",
    "\t1. 平稳性分析\n",
    "    \t1. 时序图检验\n",
    "        2. 单位根检验（三张不同形式\n",
    "    2. word分解定理\n",
    "    3. 纯随机性检验，构建Q统计，判断是否白噪声\n",
    "3. 第三章\n",
    "\t1. AR MA ARMA 模型（结构\n",
    "    2. 平稳预判别，特征根判别\n",
    "    3. 一阶两阶的adf，padf系数\n",
    "    4. MA的可逆判断\n",
    "    5. ARMA的拖尾与结尾对我们结果的判断\n",
    "    \n",
    "4. 第四章 \n",
    "\t1. 平稳时间序列的建模\n",
    "    2. 模型的识别，根据拖尾与结尾\n",
    "    3. 模型检验，总体的显著性f检验，系数的t检验。\n",
    "    4. 模型进行优化，两个模型相似我们用信息准则进行判断 # 信息准则越小越好\n",
    "    \n",
    "5. 第五章 \n",
    "\t1. 无季节效应的时间序列分析\n",
    "    2. 差分运算 \n",
    "    3. 延迟算子\n",
    "    4. d阶差分，k步差分\n",
    "    5. 非平稳时间序列建模步骤\n",
    "    6. shu系数模型\n",
    "6. 有季节效应的非平稳时间序列分析\n",
    "\t1. 简单移动平均\n",
    "    2. 指数平滑预测模型，模型的分类与选择\n",
    "7. 条件异方差（就一个选择题）\n",
    "\t1.什么是条件异方差，怎么检验条件异方差\n",
    "8. 多元序列分析\n",
    "\t1. 什么是协整？协整的基本理论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3125c80",
   "metadata": {},
   "outputs": [],
   "source": []
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